I have been coding with Python for several years and I only recently took the time to learn about Python environments. Now that I understand them, I can see why they are so useful, especially in this day and age of large team collaborations, research reproducibility, and sharing data and code when research results are published. The purpose of this
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In the previous posts, we learned how to use python scripts and modules to avoid repeating ourselves when we write programs. This is important. If you subsequently discover an error in your code (i.e. a bug), you have to fix it in only one place. Problems arise when you have to find and fix the same bug in multiple places.
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Raw data are not always pretty. Data can have different patterns, be noisy, and vary from trial to trial. We usually collect data to measure, or more accurately, estimate an effect or phenomenon. At times, it is necessary to fit a mathematical expression to raw data in order to estimate the underlying effect or phenomenon. We can then use this
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In our previous post we reviewed how to apply the DRY principle (don’t repeat yourself) to our code and we learned how to create Python modules to avoid repeating ourselves (i.e. cutting-and-pasting code) across scripts and programs. In this post we will see how we can use our modules as a stand-alone program as well as useful functions that can
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We previously learned to create our own Python functions to reduce how much we repeat ourselves in our code. In this post we see another example of the DRY principle (don’t repeat yourself) and we will learn how to ensure we don’t repeat ourselves between the different programs we write. A typical (bad) script to process data Below is an
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Learning to program is not easy. We have to learn a new language and a new way of thinking. We have to learn the grammatical rules of the programming language we are learning, and the logic of our program, how it will work its way through the code and hopefully give us the answer we expect. In this post we
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